News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter's algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency-Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency.

Online social networks have been widely engaged as rich potential platforms to predict election outcomes' in several countries of the world. The vast amount of readilyavailable data on such platforms, coupled with the emerging power of natural language processing algorithms and tools, have made it possible to mine and generate foresight into the possible directions of elections' outcome. In this paper, lexicon-based public emotion mining and sentiment analysis were conducted to predict win in the 2019 presidential election in Nigeria. 224,500 tweets, associated with the two most prominent political parties in Nigeria, People's Democratic Party (PDP) and All Progressive Congress (APC), and the two most prominent presidential candidates that represented these parties in the 2019 elections, Atiku Abubakar and Muhammadu Buhari, were collected between 9th October 2018 and 17th December 2018 via the Twitter's streaming API. tm and NRC libraries, defined in the 'R' integrated development environment, were used for data cleaning and preprocessing purposes. Botometer was introduced to detect the presence of automated bots in the preprocessed data while NRC Word Emotion Association Lexicon (EmoLex) was used to generate distributions of subjective public sentiments and emotions that surround the Nigerian 2019 presidential election. Emotions were grouped into eight categories (sadness, trust, anger, fear, joy, anticipation, disgust, surprise) while sentiments were grouped into two (negative and positive) based on Plutchik's emotion wheel. Results obtained indicate a higher positive and a lower negative sentiment for APC than was observed with PDP. Similarly, for the presidential aspirants, Atiku has a slightly higher positive and a slightly lower negative sentiment than was observed with Buhari. These results show that APC is the predicted winning party and Atiku as the most preferred winner of the 2019 presidential election. These predictions were corroborated by the actual election results as APC emerged as the winning party while Buhari and Atiku shared very close vote margin in the election. Hence, this research is an indication that twitter data can be appropriately used to predict election outcomes and other offline future events. Future research could investigate spatiotemporal dimensions of the prediction.

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The sudden rise in rural-to-urban migration has been a key challenge threatening food security and most especially the survival of Rabbit Farming and Production (RFP) in Sub-Saharan Africa. Currently, significant knowledge of RFP is going into extinction as evident by the drastic fall in commercial rabbit farming and production indices. Hence, the need for a system to proactively preserve RFP knowledge for future potential farmers cannot be overemphasized. To this end, knowledge archiving and management are key concepts of ensuring long-term digital storage of conceptual blueprints and specifications of systems, methods and frameworks with capacity for future updates while making such information readily accessible to relevant stakeholders on demand. Therefore, a reproducible Rabbit production' Knowledge Archiving and Management System (Rab-KAMS) is developed in this paper. A 3-staged approach was adopted to develop the Rab-KAMS. This include a knowledge gathering and conceptualization stage; a knowledge revision stage to validate the authenticity and relevance of the gathered knowledge for its intended purpose and a prototype design stage adopting the use of unified modelling language conceptual workflows, ontology graphs and frame system. For seamless accessibility and ubiquitous purposes, the design was implemented into a mobile application having interactive end-users' interfaces developed using XML and Java in Android 3.0.2 Studio development environment while adopting the V-shaped software development model. The qualitative evaluation results obtained for Rab-KAMS based on users' rating and reviews indicate a high level of acceptability and reliability by the users. It also indicates that relevant RFP knowledge were correctly captured and provided in a userfriendly manner. The developed Rab-KAMS could offer seamless acquisition, representation, organization and mining of new and existing verified knowledge about RFP and in turn contributing to food security.

Difficulties inherent in human face recognition from unconstrained and motion characterized scenes are usually accounted for due to varying illumination and pose of subjects in the scenes. However, a number of technical approaches have been developed to manage these nuisance factors to make recognition possible and optimal. In this paper, a review of some technical approaches to face recognition challenged with varying illumination and pose is conducted. The logical soundness of each approach and limitations are investigated while a basis for a unified, more efficient and technically optimal approach is established.